Elasticsearch recently published two new releases, and both are now available for Qbox users to upgrade their clusters.
Version 1.6.2 contains a number of bug fixes, and 1.7.1 is the latest stable release. We advise all users to upgrade.
Qbox customers should contact support to request an upgrade to either release as soon as you can accommodate it in your environment. Read this article for a short summary of the release notes and links to more information.
Ask most folks to describe Elasticsearch, and you’ll get a variety of answers. Many senior full-stack developers will struggle to provide a helpful answer. They might know how to use Elasticsearch — but it’s hard to get them to provide clear, concise, and accurate answers. This can of course create no small amount of frustration in others who need to know: What is it? What does it do? How might I benefit?
Well, we’ve got answers for you. Right here in this article. Comprehensive, yet easy for almost anyone to read. Enjoy. And ….. you’re welcome!
Elasticsearch continues to evolve. The big news recently is that release 2.0 is around the corner. Pipeline aggregations is perhaps the most interesting feature set that will be available in this upcoming release. This will be an extension of the existing ES aggregations framework, and it will provide for a number of computation types that users can perform on top of the standard aggregations results.
In this article, we give a brief overview of this ES feature extension, direct you to tutorials on aggregations, and provide links to more information.
Here’s a twist on the old adage: A ounce of prevention is worth a kiloton of user satisfaction.
It’s no secret that we’re big fans of Elasticsearch. But we’ve seen more than a few customers crash their clusters—in a variety of ways. Most of those failures are quite preventable. It’s often a matter of a simple misunderstanding, and the remedy is usually fairly easy to apply.
As we did in our recent article on field data, we invite you to think through a number of other potential problems. With little effort, you can apply several key practices that will improve stability and improve performance for your ES cluster.
In Elasticsearch, field data is generated at query time by reading the index, inverting that data structure, and then storing the results in memory. This operation can be quite slow, and it often consumes far too much valuable heap space. Before you know it, your cluster is grinding slowly toward a state of complete lethargy — but it need not be so! In this article, we present a summary of a recent Elastic article on the challenges and corresponding remedies for overgrown field data.